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2f95f81be7
* feat: apply post processing to chart data * Fix tests and lint * Fix lint * trigger tests
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""
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Functions to reproduce the post-processing of data on text charts.
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Some text-based charts (pivot tables and t-test table) perform
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post-processing of the data in Javascript. When sending the data
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to users in reports we want to show the same data they would see
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on Explore.
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In order to do that, we reproduce the post-processing in Python
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for these chart types.
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"""
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from typing import Any, Callable, Dict, Optional, Union
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import pandas as pd
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from superset.utils.core import DTTM_ALIAS, extract_dataframe_dtypes, get_metric_name
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def pivot_table(
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result: Dict[Any, Any], form_data: Optional[Dict[str, Any]] = None
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) -> Dict[Any, Any]:
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"""
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Pivot table.
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"""
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for query in result["queries"]:
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data = query["data"]
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df = pd.DataFrame(data)
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form_data = form_data or {}
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if form_data.get("granularity") == "all" and DTTM_ALIAS in df:
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del df[DTTM_ALIAS]
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metrics = [get_metric_name(m) for m in form_data["metrics"]]
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aggfuncs: Dict[str, Union[str, Callable[[Any], Any]]] = {}
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for metric in metrics:
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aggfunc = form_data.get("pandas_aggfunc") or "sum"
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if pd.api.types.is_numeric_dtype(df[metric]):
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if aggfunc == "sum":
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aggfunc = lambda x: x.sum(min_count=1)
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elif aggfunc not in {"min", "max"}:
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aggfunc = "max"
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aggfuncs[metric] = aggfunc
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groupby = form_data.get("groupby") or []
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columns = form_data.get("columns") or []
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if form_data.get("transpose_pivot"):
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groupby, columns = columns, groupby
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df = df.pivot_table(
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index=groupby,
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columns=columns,
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values=metrics,
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aggfunc=aggfuncs,
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margins=form_data.get("pivot_margins"),
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)
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# Re-order the columns adhering to the metric ordering.
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df = df[metrics]
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# Display metrics side by side with each column
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if form_data.get("combine_metric"):
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df = df.stack(0).unstack().reindex(level=-1, columns=metrics)
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# flatten column names
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df.columns = [" ".join(column) for column in df.columns]
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# re-arrange data into a list of dicts
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data = []
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for i in df.index:
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row = {col: df[col][i] for col in df.columns}
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row[df.index.name] = i
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data.append(row)
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query["data"] = data
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query["colnames"] = list(df.columns)
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query["coltypes"] = extract_dataframe_dtypes(df)
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query["rowcount"] = len(df.index)
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return result
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post_processors = {
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"pivot_table": pivot_table,
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}
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